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Changelog

Source: NEWS.md

baselinr 0.6.0

  • New wwc_robustness(): reports how stable a baseline-equivalence verdict is to the computation choices an analyst could defensibly make differently (standardizing by the pooled versus the comparison-group standard deviation, and applying the WWC small-sample correction or not), flagging whether each covariate’s category, and the overall verdict, changes. A multiverse view of a single WWC determination.
  • New attrition_boundary(): classifies a study as low or high attrition against the WWC attrition boundary (Standards Handbook v4.1, Table II.1), under the cautious or optimistic assumption. Complements attrition(), which reports the rates but leaves the classification to the user.
  • New wwc_rating(): applies the WWC group-design rating logic (Standards Handbook v4.1, Section II) to attrition and baseline equivalence, returning “Meets Without Reservations”, “Meets With Reservations”, or “Does Not Meet”.
  • New cluster_correction(): the WWC clustering correction for mismatched analyses (Procedures Handbook v4.1, Appendix F, after Hedges 2007). Corrects the t statistic and its degrees of freedom for clustering and returns the clustering-corrected p value and significance decision, with the WWC default ICCs (0.20 achievement, 0.10 other). Validated against 1,800+ clustered findings in the official WWC study database.
  • Documented that cox_index() follows Procedures Handbook v4.1 [VI.1.2] exactly: the Cox index carries no small-sample correction (that correction applies to Hedges’ g only).
  • hedges_g() and cox_index() now handle missing values consistently when na.rm = FALSE: both reject missing input up front with the message “Missing values present; set na.rm = TRUE to drop them.” Previously hedges_g() raised an opaque internal error while cox_index() silently returned NA (#12).

baselinr 0.5.0

CRAN release: 2026-06-30

  • New wwc_summary(): collapse a baseline_equivalence() table into an overall WWC verdict (satisfied / satisfied_with_adjustment / not_satisfied) plus per-category counts and the largest absolute effect size.
  • New attrition(): overall and differential attrition for a two-group design, the inputs to the WWC attrition standard.

baselinr 0.4.0

  • New bundled dataset tutoring: a simulated quasi-experimental tutoring evaluation (400 students) whose covariates span all three WWC equivalence categories. See data-raw/tutoring.R for how it is generated.
  • New vignette “An impact-evaluation workflow” walking from raw study data to a baseline-equivalence report, Love plot, and formatted table.

baselinr 0.3.0

  • New love_plot(): a Love plot of standardized effect sizes across covariates, with WWC threshold reference lines and points coloured by category (requires ggplot2).
  • New gt_baseline(): render a baseline equivalence table as a formatted gt table with readable labels and rounded statistics (requires gt).
  • ggplot2 and gt added to Suggests; both functions error gracefully if the package is not installed.

baselinr 0.2.0

  • New cox_index(): WWC Cox index standardized effect size for binary (dichotomous) covariates.
  • baseline_equivalence() now handles binary covariates (numeric 0/1, logical, or two-level factor) via the Cox index, in addition to continuous covariates via Hedges’ g. A covariate with exactly two unique values is treated as binary.
  • The output gains a type column ("continuous" / "binary"), and the effect-size column is renamed from hedges_g to the estimator-agnostic effect_size (breaking change). For binary covariates, mean_treatment and mean_comparison report event proportions.
  • The default covariate set now includes logical and factor columns, not only numeric ones.

Roadmap

  • Formatted output for reports (gt / flextable).
  • Love plot of standardized effect sizes across covariates.

baselinr 0.1.0

Initial scaffold.

  • hedges_g(): standardized mean difference (Hedges’ g) between a treatment and a comparison group, with the WWC small-sample correction factor.
  • wwc_classify(): classify standardized mean differences into the three WWC baseline-equivalence categories.
  • baseline_equivalence(): build a report-ready baseline equivalence table for continuous covariates.